Update rsna-2023-abdominal-trauma-detection.py

rsna-2023-abdominal-trauma-detection.py

@@ -156,27 +156,21 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
         # segmentation: 206 segmentations and the relevant imgs, train_series_meta.csv, train_dicom_tags.parquet
         # classification: 4711 all imgs, train.csv, train_series_meta.csv, train_dicom_tags.parquet
         # classification-with-mask: 206 segmentations and the relevant imgs, train.csv, train_series_meta.csv, train_dicom_tags.parquet
-        series_meta_file = dl_manager.download_and_extract(
-            urllib.parse.urljoin(_URL, "train_series_meta.csv")
-        )
-        dicom_tags_file = dl_manager.download_and_extract(
-            urllib.parse.urljoin(_URL, "train_dicom_tags.parquet")
-        )
-        labels_file = (
-            dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train.csv"))
-            if self.config.name != "segmentation"
-            else None
+        self.series_meta_df = pd.read_csv(
+            dl_manager.download_and_extract(
+                urllib.parse.urljoin(_URL, "train_series_meta.csv")
+            )
         )
-
-        series_meta_df = pd.read_csv(series_meta_file)
         if (
             self.config.name == "classification-with-mask"
             or self.config.name == "segmentation"
         ):
-            series_meta_df = series_meta_df.loc[series_meta_df["has_segmentation"] == 1]
+            self.series_meta_df = self.series_meta_df.loc[
+                self.series_meta_df["has_segmentation"] == 1
+            ]
 
             train_series_meta_df, test_series_meta_df = train_test_split(
-                series_meta_df, test_size=0.1, random_state=42, shuffle=True
+                self.series_meta_df, test_size=0.1, random_state=42, shuffle=True
             )
 
             train_img_files = dl_manager.download(
@@ -206,7 +200,7 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                 ).tolist()
             )
             test_seg_files = dl_manager.download(
-                train_series_meta_df.apply(
+                test_series_meta_df.apply(
                     lambda x: urllib.parse.urljoin(
                         _URL, f"segmentations/{int(x['series_id'])}.nii.gz"
                     ),
@@ -215,7 +209,7 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
             )
         else:
             train_series_meta_df, test_series_meta_df = train_test_split(
-                series_meta_df, test_size=0.1, random_state=42, shuffle=True
+                self.series_meta_df, test_size=0.1, random_state=42, shuffle=True
             )
 
             train_img_files = dl_manager.download(
@@ -239,44 +233,11 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
             train_seg_files = None
             test_seg_files = None
 
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "series_ids": train_series_meta_df["series_id"].tolist(),
-                    "dicom_tags_file": dicom_tags_file,
-                    "series_meta_file": series_meta_file,
-                    "labels_file": labels_file,
-                    "img_files": train_img_files,
-                    "seg_files": train_seg_files,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "series_ids": test_series_meta_df["series_id"].tolist(),
-                    "dicom_tags_file": dicom_tags_file,
-                    "series_meta_file": series_meta_file,
-                    "labels_file": labels_file,
-                    "img_files": test_img_files,
-                    "seg_files": test_seg_files,
-                },
-            ),
-        ]
+        dicom_tags_file = dl_manager.download_and_extract(
+            urllib.parse.urljoin(_URL, "train_dicom_tags.parquet")
+        )
 
-    def _generate_examples(
-        self,
-        series_ids,
-        dicom_tags_file,
-        series_meta_file,
-        labels_file,
-        img_files,
-        seg_files,
-    ):
-        series_meta_df = pd.read_csv(series_meta_file)
-        dicom_tags_df = datasets.load_dataset("parquet", data_files=dicom_tags_file)[
-            "train"
-        ].to_pandas()[
+        dicom_tags_df = pd.read_parquet(dicom_tags_file)[
             [
                 "SeriesInstanceUID",
                 "PixelRepresentation",
@@ -284,6 +245,17 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                 "BitsStored",
             ]
         ]
+
+        # dicom_tags_df = datasets.load_dataset("parquet", data_files=dicom_tags_file)[
+        #     "train"
+        # ].to_pandas()[
+        #     [
+        #         "SeriesInstanceUID",
+        #         "PixelRepresentation",
+        #         "BitsAllocated",
+        #         "BitsStored",
+        #     ]
+        # ]
         dicom_tags_df["SeriesID"] = dicom_tags_df["SeriesInstanceUID"].apply(
             lambda x: int(x.split(".")[-1])
         )
@@ -297,19 +269,52 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                 "BitsStored": "bits_stored",
             }
         )
-        series_meta_df = pd.merge(
-            left=series_meta_df, right=dicom_tags_df, how="inner", on="series_id"
+        self.series_meta_df = pd.merge(
+            left=self.series_meta_df, right=dicom_tags_df, how="inner", on="series_id"
+        )
+
+        labels_file = (
+            dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train.csv"))
+            if self.config.name != "segmentation"
+            else None
         )
-        labels_df = (
+        self.labels_df = (
             pd.read_csv(labels_file) if self.config.name != "segmentation" else None
         )
 
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "series_ids": train_series_meta_df["series_id"].tolist(),
+                    "img_files": train_img_files,
+                    "seg_files": train_seg_files,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "series_ids": test_series_meta_df["series_id"].tolist(),
+                    "img_files": test_img_files,
+                    "seg_files": test_seg_files,
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self,
+        series_ids,
+        img_files,
+        seg_files,
+    ):
         if self.config.name == "segmentation":
             for key, (series_id, img_path, seg_path) in enumerate(
                 zip(series_ids, img_files, seg_files)
             ):
                 series_meta = (
-                    series_meta_df.loc[series_meta_df["series_id"] == series_id]
+                    self.series_meta_df.loc[
+                        self.series_meta_df["series_id"] == series_id
+                    ]
                     .iloc[0]
                     .to_dict()
                 )
@@ -332,13 +337,15 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                 zip(series_ids, img_files, seg_files)
             ):
                 series_meta = (
-                    series_meta_df.loc[series_meta_df["series_id"] == series_id]
+                    self.series_meta_df.loc[
+                        self.series_meta_df["series_id"] == series_id
+                    ]
                     .iloc[0]
                     .to_dict()
                 )
                 patient_id = series_meta["patient_id"]
                 label_data = (
-                    labels_df.loc[labels_df["patient_id"] == patient_id]
+                    self.labels_df.loc[self.labels_df["patient_id"] == patient_id]
                     .iloc[0]
                     .to_dict()
                 )
@@ -390,13 +397,15 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
         else:
             for key, (series_id, img_path) in enumerate(zip(series_ids, img_files)):
                 series_meta = (
-                    series_meta_df.loc[series_meta_df["series_id"] == series_id]
+                    self.series_meta_df.loc[
+                        self.series_meta_df["series_id"] == series_id
+                    ]
                     .iloc[0]
                     .to_dict()
                 )
                 patient_id = series_meta["patient_id"]
                 label_data = (
-                    labels_df.loc[labels_df["patient_id"] == patient_id]
+                    self.labels_df.loc[self.labels_df["patient_id"] == patient_id]
                     .iloc[0]
                     .to_dict()
                 )